Wavelet‐based fundamental heart sound recognition method using morphological and interval features

Autor: K. P. Soman, K. I. Ramachandran, V. Nivitha Varghees
Rok vydání: 2018
Předmět:
interval features
PASCAL HSs Challenge
lcsh:Medical technology
phonocardiography
Computer science
HS patterns
Speech recognition
phonocardiogram
0206 medical engineering
Word error rate
Health Informatics
02 engineering and technology
01 natural sciences
WFHSR method
Wavelet
Health Information Management
HS delineation
high-frequency noises
PCG signal
medical signal processing
amplitude-dependent thresholding rule
Sound recognition
PhysioNet/CinC HS Challenge
Phonocardiogram
business.industry
feature extraction
010401 analytical chemistry
eGeneralMedical databases
Wavelet transform
Pattern recognition
wavelet-based fundamental heart sound recognition method
020601 biomedical engineering
Thresholding
0104 chemical sciences
murmurs
synchrosqueezing wavelet transform
wavelet transforms
Shannnon energy envelope
morphological features
lcsh:R855-855.5
decision-rule algorithm
Heart sounds
Artificial intelligence
business
low-frequency noises
Zdroj: Healthcare Technology Letters (2018)
ISSN: 2053-3713
DOI: 10.1049/htl.2016.0109
Popis: Accurate and reliable recognition of fundamental heart sounds (FHSs) plays a significant role in automated analysis of heart sound (HS) patterns. This Letter presents an automated wavelet-based FHS recognition (WFHSR) method using morphological and interval features. The proposed method first performs the decomposition of phonocardiogram (PCG) signal using a synchrosqueezing wavelet transform to extract the HSs and suppresses the murmurs, low-frequency and high-frequency noises. The HS delineation (HSD) is presented using Shannnon energy envelope and amplitude-dependent thresholding rule. The FHS recognition (FHSR) is presented using interval, HS duration and envelope area features with a decision-rule algorithm. The performance of the method is evaluated on PASCAL HSs Challenge, PhysioNet/CinC HS Challenge, eGeneralMedical databases and real-time recorded PCG signals. Results show that the HSD approach achieves an average sensitivity (Se) of 98.87%, positive predictivity (Pp) of 97.50% with detection error rate of 3.67% for PCG signals with signal-to-noise ratio of 10 dB, and outperforms the existing HSD methods. The proposed FHSR method achieves a Se of 99.00%, Sp of 99.08% and overall accuracy of 99.04% on both normal and abnormal PCG signals. Evaluation results show that the proposed WFHSR method is able to accurately recognise the S1/S2 HSs in noisy real-world PCG recordings with murmurs and other abnormal sounds.
Databáze: OpenAIRE
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